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In this paper, we report on the observation of nonlinear effects in a nanostrip phononic metasurface (NPM) that enable the tuning of resonance frequencies at 1.42 GHz. The NPM resonator made of a periodic nanostrip array is fabricated on a lithium niobate substrate. Each of the nanostrips is 250 nm wide and is made of a 680-nm-thick SiO2 layer stacking on 50-nm Al metal electrodes. Finite element analysis reveals that the device operates in a vertically polarized (compression) mode with substantial acoustic energy confined in the nanostrips, leading to a local resonance at low acoustic velocity. Due to the nonlinearity, the resonance frequency of the device decreases with the increase in stimulation power. The underlying mechanism of the nonlinearity is found to be the power-dependent coupling of the adjacent nanostrips. This coupling induces softening of the substrate surface region, which reduces the acoustic velocity and, hence, the bulk radiation. As a result, the quality factor of the NPM resonator is found to improve with the increase in stimulation power. The power-dependent coupling of nanostrips in the NPM resonator demonstrates a reliable method for the realization of nonlinearity in phononic metasurfaces, which would significantly enrich the mechanisms for the manipulation of surface acoustic waves at high frequencies. [ABSTRACT FROM AUTHOR]

After nine years (the maximum three consecutive terms) on the Editorial Board of the IEEE TRANSACTIONS on ELECTRON DEVICES (T-ED), Dr. John Tower of Sarnoff Corporation, Prof. Jagadesh Kumar of the Indian Institute of Technology, and Dr. Hervé Jaouen of ST Microelectronics have stepped down from their Editor positions in Image Sensors (John) and MOS Devices and Technology (Hervé and Jagadesh). After six years on the Editorial Board of T-ED, Prof. Adrian Ionescu from the École Polytechnique Fédérale de Lausanne has left his T-ED Editor position in Solid-State Sensors and Actuators. Finally, after three years of service on the T-ED Editorial Board, Dr. David Gundlach of the National Institute of Standards and Technology and Prof. Navakanta Bhat of the Indian Institute of Science have stepped down from their T-ED Editor positions in Molecular and Organic Devices and MOS Devices and Technology, respectively. I would like to express my sincere gratitude—and the gratitude of the Electron Device Society community—to John, Hervé, Jagadesh, Adrian, David, and Navakanta for their commitment, dedication, and hard work during their terms of service. Our very best wishes to them in their future endeavors. [ABSTRACT FROM AUTHOR]

Phononic crystals (PnCs) exhibit acoustic properties that are not usually found in natural materials, which leads to the possibility of new devices for the complex manipulation of acoustic waves. In this article, a micron‐scale phononic waveguide constructed by line defects in PnCs to achieve on‐chip, tightly confined guiding, bending, and splitting of surface acoustic waves (SAWs) is reported. The PnC is made of a square lattice of periodic nickel pillars on a piezoelectric substrate. The PnC lattice constant, pillar diameter, and pillar height are set to 10, 7.5, and 3.2 µm, respectively, leading to a complete bandgap centered at 195 MHz. Interdigitated transducers are monolithically integrated on the same substrate for SAW excitation. The guiding, bending, and splitting of SAWs in the phononic waveguide are experimentally observed through measurement of the out‐of‐plane displacement fields using a scanning optical interferometer. The combination of destructive interference due to the Bragg bandgap and the interaction of the propagating wave with the pillars around the channel results in a tight confinement of the displacement field. The proposed phononic waveguides demonstrate the feasibility of precise local manipulation of SAW that is essential for emerging frontier applications, notably for phonon‐based classical and quantum information processing. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a pseudo dynamic buffer (PDB) for footed domino logic circuit implementation. Using the proposed PDB structure, the output pulse during the precharge process is prevented from propagating to the output stage, as is the case in conventional case. As a result, up to half of the power is saved compared to a conventional domino gate, while improving the sampling window of the dynamic gate. This PDB structure is applicable not only for Pull-down network (N-type) dynamic logic, but also for Pull-up networks (P-type). Simulation results illustrate improved performance using the proposed scheme compared to the conventional dynamic logic for different loading conditions, clock frequencies and logic functions. In addition, our proposed design reduces the clock loading from conventional three to two transistors. As a result, the proposed scheme significantly saves power due to lower load capacitance on the clock bus. Test structures are fabricated in CMOS technology. Measurement results validate the proposed concept and illustrate power saving as compared to conventional design. [Copyright &y& Elsevier]

Abstract: This paper presents a monolithic 4×4 tin oxide gas sensor array together with on-chip multiplexing and differential read-out circuitry. A robust fabrication process focusing on the integration of the CMOS circuitry and the microelectromechanical systems (MEMS) structure is first described. The gas sensor uses a surface micro-machined micro-hotplate based structure. Platinum is selected as the material of choice for both the micro heater and the sensor’s electrodes, for its good thermal stability and compatibility with the sensing film. Different post-treatments are used to modify the characteristics of the tin oxide gas sensors hence improving the sensor’s selectivity of the overall sensor array. Furthermore, in contrast to the conventional voltage divider read-out technique, a novel differential read-out circuit (DRC) for tin oxide gas sensors is proposed. The DRC applies a constant current to drive the sensor and uses a unit-gain single stage amplifier (inverter) to generate a fully differential output, directly from the voltage drop across the sensor. The output of the DRC is simply proportional to the difference between the voltage on the two electrodes of the sensor but not to the transistor parameters such as mobility and threshold voltage, neither to the supply voltage. The monolithic sensor array and its pre-processing circuitry have been implemented in our in-house 5μm process. Experimental results showed good linearity at the output of the DRC for a wide range of sensor resistance variation (over 20MΩ). The fabricated micro-hotplate sensor array was tested for four target gases. Results show good sensitivity and interesting thermal characteristic leading to only 15.5mW power consumption for 300°C operating temperature. [Copyright &y& Elsevier]

Gas identification represents a big challenge for pattern recognition systems due to several particular problems such as nonselectivity and drift. The purpose of this paper is twofold: 1) to compare the accuracy of a range of advanced and classical pattern recognition algorithms for gas identification for the in. house sensor array signals and 2) to propose a gas identification ensemble machine (GIEM), which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. An integrated sensor array has been designed with the aim of identifying combustion gases. The classification accuracy of different density models is compared with several neural network architectures. On the gas sensors data used in this paper, Gaussian mixture models achieved the best performance with higher than 94% accuracy. A committee machine is implemented by assembling the outputs of these gas identification algorithms through advanced voting machines using a weighting and classification confidence function. Experiments on real sensors' data proved the effectiveness of the system with an improved accuracy over the individual classifiers. An average performance of 97% was achieved using the proposed committee machine. [ABSTRACT FROM AUTHOR]

In this paper, a novel gas identification approach based on Gaussian process (GP) combined with principal components analysis is proposed. The effectiveness of this approach has been successfully demonstrated on an experimentally obtained dataset. Our aim is the identification of different gases with an array of commercial Taguchi gas sensors (TGS) as well as microelectronic gas sensors. The proposed approach is shown to outperform both K nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. [ABSTRACT FROM AUTHOR]

This paper presents a digital pixel sensor (DPS) array employing a time domain analogue-to-digital conversion (ADC) technique featuring adaptive dynamic range and programmable pixel response. The digital pixel comprises a photodiode, a voltage comparator, and an 8-bit static memory. The conversion characteristics of the ADC are determined by an array-based digital control circuit, which linearizes the pixel response, and sets the conversion range. The ADC response is adapted to different lighting conditions by setting a single clock frequency. Dynamic range compression was also experimentally demonstrated. This clearly shows the potential of the proposed technique in overcoming the limited dynamic range typically imposed by the number of bits in a DPS. A 64 × 64 pixel array prototype was manufactured in a 0.35-μm, five-metal, single poly, CMOS process. Measurement results indicate a 100 dB dynamic range, a 41-s mean dark time and an average current of 1.6 μA per UPS. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors’ data proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, Generative topographic mapping, Probabilistic PCA mixture and K nearest neighbors. On our gas sensors data, the best performance was achieved by Gaussian mixture models. [Copyright &y& Elsevier]

A novel ultra-low power operating technique is presented for Mega-pixels current-mediated CMOS imagers. In the proposed technique, the reset and read-out phases occur simultaneously: as a single pixel is being read-out another pixel is being reset Such a strategy reduces power consumption by more than 2 orders of magnitude for current- mediated Mega-pixels CMOS imagers, while still allowing for on-read-out fixed pattern noise correction. Power consumption becomes independent of both read-out speed and imager array size. Additionally, the proposed operating technique leads to improved linearity for the pixel response, increased dynamic range as well as on-chip digital shutter functionality. A single additional address decoder is required to generate the reset signals, resulting in a marginal increase in silicon area. The wiring overhead is kept to a minimum with only a single control signal required to operate the current- mediated CMOS imager. [ABSTRACT FROM AUTHOR]

A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote. Such a combination improves generalization performance but can require large amounts of memory and computation, a serious drawback for addressing portable real-time pattern recognition applications. We report here a compact three-dimensional (3-D) multiprecision very large-scale integration (VLSI) implementation of a bagging ensemble. In our circuit, individual classifiers are decision trees implemented as threshold networks-one layer of threshold Logic units (TLU5) followed by combinatorial logic functions. The hardware was fabricated using 0.7-μm CMOS technology and packaged using MCM-V micro-packaging technology. The 3-D chip implements up to 192 TLUs operating at a speed of up to 48GCPPS and implemented in a volume of (w × L × h) = (2 × 2 × 0.7) cm3. The 3-D circuit features a high level of programmability and flexibility offering the possibility to make an efficient use of the hardware resources in order to reduce the power consumption. Successful operation of the 3-D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application. [ABSTRACT FROM AUTHOR]

In this paper we propose an on-pixel Analogue-to-Digital Converter (ADC) based on pulse frequency modulation (PFM) scheme. This PFM based converter presents a viable solution for pixel-level based ADC. It uses a simple and robust circuit that can be implemented in a compact area resulting in a 23% fill-factor for a digital vision sensor in 0.25 μm CMOS technology. An in-built light adaptation mechanism has also been implemented which allows the sensor to better adapt to low-light intensity or to adjust the sensor saturation level. As a consequence, the proposed sensor features a programmable dynamic range. Image lag is eliminated since a reset of the photodetector is performed after the conversion period. A prototype comprising a 32×32 pixel array has been implemented in CMOS 0.25 μm technology. Each pixel occupies an area of 45×45 μm2 with an average power consumption of 85 μW per pixel. [Copyright &y& Elsevier]

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria. [ABSTRACT FROM AUTHOR]

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons. [ABSTRACT FROM AUTHOR]

Soft transparent electrodes (TEs) have received tremendous interest from academia and industry due to the rapid development of lightweight, transparent soft electronics. Metallic micro‐nano networks (MMNNs) are a class of promising soft TEs that exhibit excellent optical and electrical properties, including low sheet resistance and high optical transmittance, as well as superior mechanical properties such as softness, robustness, and desirable stability. They are genuinely interesting alternatives to conventional conductive metal oxides, which are expensive to fabricate and have limited flexibility on soft surfaces. This review summarizes state‐of‐the‐art research developments in MMNN‐based soft TEs in terms of performance specifications, fabrication methods, and application areas. The review describes the implementation of MMNN‐based soft TEs in optoelectronics, bioelectronics, tactile sensors, energy storage devices, and other applications. Finally, it presents a perspective on the technical difficulties and potential future possibilities for MMNN‐based TE development. [ABSTRACT FROM AUTHOR]

• Transpiration for cucumber crops grown in greenhouses with CO 2 enrichment was better predicted using a combined physiological and hyperspectral data driven method. • The prediction accuracy of the models significantly improved by adding hyperspectral based vegetation indices (NDVI, PRI, WBI) to microclimate parameters as inputs. • The comparative assessment for modelling and prediction of transpiration was conducted using three different machines learning models: DNN, XGBoost, and SVR. • The XGBoost model with the inclusion of vegetation indices as model features is recommended to predict transpiration of cucumber crops grown in greenhouses under the CO 2 enrichment regime. Accurate prediction of transpiration in protected agricultural settings with CO 2 enrichment is of high importance for the deployment of precision irrigation. Several physical and mechanistic based models are deployed for the estimation of transpiration. However, these models may not accurately explain physiological interactions for greenhouse settings under CO 2 enrichment. Thus, it is necessary to build site-specific and microclimate-targeted models that can accurately estimate transpiration and irrigation water requirements. In this work, a data-driven predictive model is proposed for the estimation of short-term transpiration (mmol/m2.s) for cucumber crops grown in greenhouses with CO 2 enrichment. Three machine learning models were investigated for transpiration modelling and prediction: deep neural networks (DNN), extreme gradient boosting (XGBoost), and support vector machine regression (SVR). These predictive models assimilate microclimate, physiological and hyperspectral features with high temporal and spatial resolutions. The results demonstrated the inclusion of hyperspectral-based vegetation indices significantly increased the performance of the three machine learning models in predicting transpiration. The XGBoost model outperformed the DNN and SVR models with higher R2 of 0.62 – 7.53%, lower RMSE and MAE values of 9.74 – 46.19 mmol/m2.s and 13.74 – 39.08 mmol/m2.s respectively with microclimate, physiological and hyperspectral based vegetation index inputs. Moreover, the time series prediction with different time steps improved the XGBoost model, achieving an R2 of up to 99.0%. The XGBoost proved to be the most efficient in predicting transpiration for cucumbers grown in greenhouses under CO 2 enriched environments. [ABSTRACT FROM AUTHOR]

• An analytical and data-driven model is developed to analyse the greenhouse. • Data-driven artificial neural network is used as the prediction model. • An RMPC based on the minimax approach is adopted. • RMPC results in superior temperature control and energy consumption. The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set. [ABSTRACT FROM AUTHOR]

Highlights • Dual transduction is implemented on a single sensor to achieve gas identification. • Simultaneous detection of the resistance and mass variations in the sensing material is achieved by a single sensor. • The sensing range of the sensing material is expanded through dual transduction. • The dual transduction method can help fully explore the potential of conductive gas sensing materials. Abstract Poor selectivity is one important limitation of gas sensors based on chemisorption or physisorption. This poor selectivity is usually tackled through the use of a gas sensor array, whose individual elements have diverse selectivity. Gas identification can be achieved by analyzing the unique response patterns of the sensor array. However, such an approach is costly due to its complexity. In this paper, we investigate an alternative solution enabling single sensor gas identification through the integration of two transduction mechanisms in one sensor. We demonstrate this approach through the design of a novel dual transduction gas sensor capable of simultaneously detecting gas-induced variations in the mass and resistance of the sensing material. The relative independence of acquired gravimetric and resistive responses is shown to enable subsequent gas identification. In addition, the proposed approach can also improve the overall limit of detection (LOD) to different gases by exploiting the individual responses with the best LODs. The proposed dual transduction gas sensor was implemented by integrating a film bulk acoustic wave resonator (FBAR) together with interdigitated electrodes. A configurable dual-mode oscillator circuit is also proposed to simultaneously read-out both responses from the sensor. [ABSTRACT FROM AUTHOR]

While deep learning algorithms have advanced to a great extent, they are all designed for frame-based imagers that capture images at a high frame rate, which leads to a high storage requirement, heavy computations, and very high power consumption. Unlike frame-based imagers, event-based imagers output asynchronous pixel events without the need for global exposure time, therefore lowering both power consumption and latency. In this paper, we propose an innovative image recognition technique that operates on image events rather than frame-based data, paving the way for a new paradigm of recognizing objects prior to image acquisition. To the best of our knowledge, this is the first time such a concept is introduced featuring not only extreme early image recognition but also reduced computational overhead, storage requirement, and power consumption. Our collected event-based dataset using CeleX imager and five public event-based datasets are used to prove this concept, and the testing metrics reflect how early the neural network (NN) detects an image before the full-frame image is captured. It is demonstrated that, on average for all the datasets, the proposed technique recognizes an image 38.7 ms before the first perfect event and 603.4 ms before the last event is received, which is a reduction of 34% and 69% of the time needed, respectively. Further, less processing is required as the image is recognized 9460 events earlier, which is 37% less than waiting for the first perfectly recognized image. An enhanced NN method is also introduced to reduce this time. [ABSTRACT FROM AUTHOR]

The human body's temperature is one of the most important vital markers due to its ability to detect various diseases early. Accurate measurement of this parameter has received considerable interest in the healthcare sector. We present a novel study on the optimization of a temperature sensor based on silver interdigitated electrodes (IDEs) and carbon-sensing film. The sensor was developed on a flexible Kapton thin film first by inkjet printing the silver IDEs, followed by screen printing a sensing film made of carbon black. The IDE finger spacing and width of the carbon film were both optimized, which considerably improved the sensor's sensitivity throughout a wide temperature range that fully covers the temperature of human skin. The optimized sensor demonstrated an acceptable temperature coefficient of resistance (TCR) of 3.93 × 10−3 °C−1 for temperature sensing between 25 °C and 50 °C. The proposed sensor was tested on the human body to measure the temperature of various body parts, such as the forehead, neck, and palm. The sensor showed a consistent and reproducible temperature reading with a quick response and recovery time, exhibiting adequate capability to sense skin temperatures. This wearable sensor has the potential to be employed in a variety of applications, such as soft robotics, epidermal electronics, and soft human–machine interfaces. [ABSTRACT FROM AUTHOR]

This paper reports a global digital controller for multi-channel high density micro-stimulator applications. By centralizing the essential functions including stimulation timing controls, tissue-electrode impedance (TEI) monitoring and power supply modulation in the global digital controller (GDC), digital blocks in the local electrode driver channels (LEDCs) can be greatly simplified, resulting in compact electrode drivers suitable for high density intracellular stimulations. Based on the proposed column-parallel row-scanning (CPRS) stimulator topology, the 5-wire global control ensures customized per channel stimulation with minimal interfacing overhead. A 4-column GDC stimulator prototype is fabricated in a 0.18 µm CMOS process. It features flexible stimulation with a power consumption of 18 µW/column while enabling global power-supply modulation and real-time TEI monitoring for improved stimulation efficiency and safety. [ABSTRACT FROM AUTHOR]

A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node. [ABSTRACT FROM AUTHOR]

In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralow manufacturing cost, the deposited material’s morphology can be accurately controlled with fabricated general-purpose AAO template. Compared with nonstandard lithography-based template fabrication method, the need of complicated Bosch etching process and its associated complex process parameter tuning is eliminated. As a result, the cost-effective mass production of 3-D nanotemplate-based material and devices can be enabled. In addition, the target material’s limited coverage and time efficiency issues widely existing in the previous deposition methods are well-addressed by our customized USP deposition, especially for the 3-D nanotemplate with large surface-to-volume ratio, leading to significantly improved gas-sensing performance. Moreover, the proposed fabrication recipe, together with the adopted gas recognition algorithms based on linear discriminant analysis (LDA), is validated based on the reported extensive measurement results for five gas biomarkers widely exploited for patients’ exhaled gas-sensing and recognition applications. This shows great potential for the early disease diagnose of diabetes, breast cancer, acute lung injury, colon diseases, lung cancer, and so on. [ABSTRACT FROM AUTHOR]

This paper presents a low-power column-parallel inverter-based cyclic analog-to-digital converter (ADC) for CMOS image sensor readout circuit. By partially floating the capacitors inside the multiply digital-analog-converter during the least significant bit (LSB) quantization, the amplifier load capacitance could be significantly scaled down, which allows much higher settling speed and shorter cycle period. Since the signal-to-noise ratio for LSB cycle is relaxed due to the residual amplification, the proposed capacitance scaling only contributes ignorable input-referred quantization noise. Using the proposed techniques, a cyclic ADC can operate under 50% power consumption without suffering conversion rate, noise performance, and linearity. A 12-b quantization resolution test chip is fabricated using the TSMC 0.18- \mu \textm technology with 110 column-parallel ADC channels and 10.08- \mu \textm \times 750 - \mu \textm column pitch. The 3.5/−2 LSBs integral nonlinearity and 10.1-b effective-number-of-bit are measured under 2- \mu \texts sampling rate with 120- \mu \textW power consumption per channel. [ABSTRACT FROM AUTHOR]

This article presents a low-power column-parallel $\Sigma \Delta $ analog-to-digital converter (ADC) with shared operational transconductance amplifiers (OTAs) for CMOS image sensors (CISs). Through the proposed time division multiplexing of OTAs, the power consumption of the modulator achieves a reduction of about 40%. The proposed structure alleviates the layout requirement of OTAs, enabling implementation of a high-resolution low-power image sensor in advanced CMOS technology nodes. As the coupled noise introduced by the proposed column-shared scheme is suppressed to a lower level than the inherent crosstalk of adjacent pixels, the proposed structure only contributes a negligible inter-column coupled noise from the ADCs. Moreover, a compact digital decimation filter with a single-bit-bit-wise-inversion (BWI) topology is also proposed, which can reduce chip area significantly. The prototype sensor is fabricated in a 40-nm standard CMOS technology with 256 $\times256$ pixel array and 256 proposed column-parallel $\Sigma \Delta $ ADCs. Each $\Sigma \Delta $ ADC occupies a core area of 4.5 $\mu \text{m}\,\,\times $ 310 $\mu \text{m}$ , while consuming a power of 58.8 $\mu \text{W}$. The measured differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.49/−0.65 LSB and +5.1/−4.8 LSB, respectively. Measurement results show a dynamic range of 79.9 dB and an effective-number-of-bit (ENOB) of 11.4 bit. This work achieves a figure of merit (FOM) of 97.2 fJ/step. [ABSTRACT FROM AUTHOR]

A compact voltage-mode bandgap voltage reference (BGR) is presented. Instead of using overhead circuits, the silicon bandgap narrowing effect is exploited for bipolar junction transistor’s (BJT) curvature reduction and residual curvature correction. Prototype measurements in a 0.18- \mu \textm standard CMOS process show that the curvature of the BJT is effectively reduced from its inherent 3.6 mV to 1.4 mV. The proposed BGR measures a minimum temperature coefficient of 8.7 ppm/ °C from −55 °C to 125 °C by batch trimming one resistor. After a curvature trimming, it further improves to 4.1 ppm/ °C. The BGR has a minimum supply voltage of 1.3 V, 4.3 \mu \textA nominal current consumption, 0.03%/V line sensitivity, and 2 mV/mA load sensitivity at 25 °. The output rms noise in the $0.1\sim 10$ -Hz band measures 10.23 \mu \text{V} . [ABSTRACT FROM PUBLISHER]

With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, greenhouses consume more resources than other commercial buildings due to their inefficient operation and structure. Therefore, a data-driven model predictive control approach for a semi-closed greenhouse is proposed for temperature control and reducing energy consumption in this study. The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. The greenhouse model's performance is evaluated under varying scenarios, such as increasing the prediction time step and changing the number of samples in the training data set. Results illustrated that the MPC approach had a better temperature control than the greenhouse adaptive control system for winter and summer with an RMSE value of 0.33 °C and 0.36 °C, respectively. Similarly, model predictive control resulted in an energy reduction of 7.70% for winter and 16.57% for the summer season. The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set. • A model predictive control approach is adopted for a semi-closed greenhouse. • A multilayer perceptron model is used for modeling the dynamic system. • Not only temperature set-point tracking but also energy consumption is considered. • MPC results in superior temperature tracking and reduction in energy consumption. [ABSTRACT FROM AUTHOR]

IoT security plays a crucial role where smart devices are interconnected and one of these can give an intrusion access to the network if they are not secured. Physical unclonable function (PUF) comes in handy as an emerging lightweight hardware security primitive for key management and device authentication. In this research work, we propose a new architecture of PUF hardware based on memristor threshold voltage variation. Memristor manufacturing variations are exploited for the threshold voltage variation which makes the device unique. The selection from the proposed crossbars forms two connected memristors. The selections connect the formed voltage divider network to the input and ground. The output is probed at the middle node, which is the selected rows of the two crossbars. Voltage (challenge) is applied at selected pair of memristors through the corresponding columns and rows, and output (response) is taken from the selected rows. We believe this work will set a new direction for the researchers to explore manufacturing errors for PUF applications. [ABSTRACT FROM AUTHOR]

In this article, we report a wafer-level packaged Pirani vacuum gauge using the proprietary InvenSense CMOS MEMS technology. The micro Pirani vacuum gauge features three serpentine-shaped molybdenum thermistors on the suspended silicon-on-insulator (SOI) bridges, while the wiring gap of each serpentine-shaped silicon microbridge is 1.6 ${ {\mu }}\text{m}$. For the vacuum range of $5\times 10^{-{4}}$ –760 Torr, the CMOS MEMS Pirani gauge configured with a constant temperature interface circuit achieves a sensitivity of 0.414 V/Torr in a very fine vacuum regime, while its heating power is less than 21.3 mW. Moreover, the measured output of the micro Pirani gauge shows good agreement with a semi-empirical model, while the model predicts that the proposed Pirani gauge can measure a vacuum pressure as low as $2.6\times 10^{-{4}}$ Torr. The performance achieved by this Pirani vacuum gauge combined with its high level of integration makes it a promising Internet of Things (IoT) sensing node for vacuum monitoring in the industry. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) presents many hazardous traits such as corrosive, explosive, toxic, and flammable. It is slightly denser than air, and a mixture of H2S and air can be volatile. Therefore, a reliable and robust measurement system is required to effectively detect and quantify H2S in many applications, such as oil and gas industries. There are several methods available in the literature to quantify H2S in fuel gases; however, only a few are available in case of air samples. Furthermore, array-based sensors are more reliable in the detection of volatile organic compounds (VOCs); however, sensor arrays are more expensive and challenging to produce. To overcome the limitations of producing physical sensor arrays, this article proposes a concept of virtual sensing that enables to augment a single sensing platform into a virtual array, thus, increasing the detection accuracy at no extra cost of producing a large physical sensors array. The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H2S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H2S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H2S; and 5) the proposed model is a highly sensitive and reliable H2S gas sensing scheme with the ability to detect the gas instantaneously. The proposed gas quantification model has the advantages of being low cost, easy to implement, and fast operation compared with the analytical solutions. Furthermore, it is extensively tested and validated using real gas data. [ABSTRACT FROM AUTHOR]

In this article, a moving accumulative sign filter (MASF) voter model and a low-power serial voting circuit are proposed for high-speed clock data recovery (CDR). Different from the previously reported parallel voter and moving average majority voter (MV), the proposed design is based on the MASF algorithm and adopts a two-stage voting structure. Only D-flip-flop (DFF) and basic logic gates are used to realize the serial voting function of the four continuous lead/lag/hold decision signals. The proposed MV realizes the signal processing of the four-phase error information without using traditional gain and quantization operations. In addition, by eliminating the redundant output state, the input noise of the digital filter of CDR can be reduced, which could also reduce the power consumption of the subsequent circuits due to less logical flips. As a result, the proposed serial MV can operate at a half baud rate in a high-speed CDR and it can reduce the chip area by removing the demultiplexer unit and the moving average filter. The proposed MV circuit is implemented for a high-speed CDR using a 40-nm CMOS process, which shows a 12-GHz maximum operating speed. The measured power consumption of the whole voter at 10 GHz is only 0.55 mW and the core chip area is $8.6\,\times \,25.4\,\,\mu \text{m}^{2}$. [ABSTRACT FROM AUTHOR]

This work presents a cost-effective shadow mask printing approach to fabricate flexible sensors. The liquid-state sensing material can be directly brushed on a flexible substrate through a shadow mask. The ink leakage issue which often occurs in printed electronics is addressed with a custom taping scheme. A simple thermal compression bonding approach is also proposed to package the functional area of the sensor. To verify the feasibility and robustness of the proposed fabrication approach, a prototyped strain gauge displacement sensor is fabricated using carbon ink as the sensing material and a flexible polyimide (PI) film as the substrate. Once the substrate is deformed, cracks in the solidified ink layer can cause an increased resistance in the conductive path, thus achieving function of stable displacement/strain sensing. As a demonstration for displacement sensing application, this sensor is evaluated by studying its real-time resistance response under both static and dynamic mechanical loading. The fabricated sensor shows a comparable performance (with a gauge factor of ~17.6) to those fabricated using costly lithography or inkjet printing schemes, while with a significantly lower production cost. [ABSTRACT FROM AUTHOR]

Spread-spectrum clocking (SSC) is an active solution to attenuate electromagnetic interference (EMI) in Gb/s serial communication systems by slightly modulating the phase-locked loop (PLL) output clock frequency. This article presents a phase-compensated spread-spectrum clock generator (SSCG) with the state-of-the-art EMI reduction and clock rms jitter performance. A 32-slice scaled resistor-based buffer is proposed to realize the phase interpolator (PI). The proposed design has no static current and low complexity without active device matching requirement. The 32-slice PI structure can achieve a simulated 22-dB EMI reduction with a 32-step triangular modulation profile. The proposed SSCG chip with a charge-pump-based fractional- $N$ radio frequency (RF)-PLL and a source-series-terminated (SST) driver is fabricated using a 55-nm CMOS process. Measurement result shows that EMI reduction of the 5-GHz output clock power spectrum is 19.44 dB under 0.5% down-spread. The rms jitters with SSC-off and SSC-on, adopting a second-order clock recovery in oscilloscope, are 630 and 640 fs, respectively. The normalized power consumption is 9.3 mW/GHz, and the core area occupation is 0.092 mm2. [ABSTRACT FROM AUTHOR]

Recently, carbon nanotube (CNT)-based micro-devices have attracted extensive attention at the frontiers of intelligent electronics, but the practical application of these devices are seriously limited by the low CNT package rates, poor heat dissipation, and intricate in-situ growth processes. Here, microelectrodes with low impedance based on in-situ-grown super-black vertical bamboo-like multi-walled CNT arrays (∼70 μm) were fabricated inside silicon via holes using face-down catalytic plasma enhanced chemical vapor deposition. Interestingly, the unique CNT array exhibited uniformly high absorption efficiency with a broad range of wavelength (exceeding 99.65% in the UV-NIR band and 99% in the mid-infrared region). Moreover, the excellent cooling performance of CNT array microelectrodes based on in-situ-grown bamboo-like CNT arrays within silicon via holes was demonstrated. Our study highlights the benefits of CNT-based micro functional devices, which indicates the integrated in-situ-grown bamboo-like CNT arrays will have potential applications in advanced microelectrodes, CNT-based field-effect transistors, and portable terahertz inspection devices. Image 1 [ABSTRACT FROM AUTHOR]

This paper presents an ultra-high-frequency (UHF) passive sense tag for electrical grid and substation thermal monitoring, with emphasis on the tag system optimization and the design of a low power embedded temperature sensor. The designed tag achieves a sensitivity of $-$ 12.3 dBm under active temperature monitoring operation, which is the state of the art among existing UHF passive temperature sense tag products. The sensing inaccuracy of the tag is $\pm$ 2.5 $^{\circ }$ C (3 $\sigma$) from $-$ 25 to 120 $^{\circ }$ C after a low-cost wireless single-point trim. An antimetal ceramic-packaged tag was tested by attaching to a ring main unit in the substation and complete tag system demonstrated robust wireless operation with a sensing distance of 3.5 m. The combination of batteryless and wireless operation, high sensitivity, wide sensing range, and small incident-power-dependent error ($\pm$ 0.2 $^{\circ }$ C) makes this tag suitable for the target applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks. [ABSTRACT FROM AUTHOR]

A self-powered device for human activity monitoring and energy harvesting for Internet of Things (IoT) devices is proposed. The self-powered device utilizes flexible Nano-generators (NGs), flexible diodes and off-the-shelf capacitors. During footsteps the NGs generate an AC voltage then it is converted into DC using rectifiers and the DC power is stored in a capacitor for powering the IoT devices. Polydimethylsiloxane (PDMS) and zinc stannate (ZnSnO3) composite is utilized for the NG active layer, indium tin oxide (ITO) and aluminum (Al) are used as the bottom and top electrodes, respectively. Four diodes are fabricated on the bottom electrode of the NG and connected in bridge rectifier configuration. A generated voltage of 18 Vpeak was achieved with a human footstep. The self-powered smart device also showed excellent robustness and stable energy scavenger from human footsteps. As an application we demonstrate human activity detection and energy harvesting for IoT devices. [ABSTRACT FROM AUTHOR]

Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers' health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics. [ABSTRACT FROM AUTHOR]